Presently, there is a vast amount of media content, such as audios, videos, or graphics, available from a variety of sources. From digital graphics and music to films or movies to broadcast television programs to cable or satellite television programs to home movies or user-created video clips to personal photographs, there are many repositories and databases from which people may choose and obtain media content in various formats, and the amount of media content available continues to grow at a very high rate. Broadcast, cable, or satellite companies often provide hundreds of different channels for viewers to choose from. Movie rental companies such as Netflix and Blockbuster offer tens, even hundreds, of thousands of titles on DVDs (digital video disc) or video cassettes. More recently, the Internet has also lent its unique capability and become a great repository and distribution channel for video media world-wide. Online stores such as Amazon.com have a great number of CDs, DVDs, and downloadable media files for sale. Websites such as YouTube and AOL Video have immense audio and video collections, often millions of audio/video clips, contributed by users from all over the world. Websites such as Flickr™ or webshorts enable users to upload their digital images, such as personal photos, to the Internet so that they may be shared among families and friends or with the public. The Internet also broadens the horizon for many other types of businesses. By conducting transactions online, businesses are able to sell to customers all over the world. At the same time, businesses are able to offer much greater variety of products, since they are no longer restricted by the spaces available in their physical stores.
With such a great amount of available products and media content, businesses, especially online businesses, often wish to recommend their products and media content to users or prospective customers, because these businesses typically offer very large numbers of products at their websites and consequently, it is unlikely for individual users or customers to browse through all available products personally. For example, a registered user at Amazon.com, after logging in, generally receives recommendations for various types of products, such as books, CDs, DVDs, consumer electronic items, etc., based on his or her purchasing and/or browsing history. Similarly, a registered user at Netflix.com, after logging in, generally receives movie recommendations based on his or her rental and/or viewing history.
Businesses usually try to make such product recommendations personally tailored to individual users or customers as much as possible, since what may be interesting to one customer may not be interesting to another customer. In order to make personalized product recommendations, it is necessary to collect various types of information concerning the individual users or customers in order to determine what a particular user may like or dislike. There are many different means to collect user information. For example, websites often require a user to register, i.e., creating an account with a login name and a password, before he or she can receive personalized recommendations. In addition, registered users are required to allow the websites to set cookies in their browsers so that the websites may collect information about the users.
One widely employed technique for making personalized product recommendations is Collaborative Filtering. Collaborative Filtering is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc. In the case of applying Collaborative Filtering to making personalized product recommendations, the collaboration is among the users or customers and the available products or media content of a system. Typically, known information is collected from the users and aggregated at a centralized location, such as a server. Patterns, such as similarities between two or more users or similarities between two or more products, among the collected data are located and used as the basis for making recommendations. Consequently, information that may be used to identify individual users and/or client devices associated with the users are also collected by the recommendation system, since the system needs to be aware of each user's personal taste in order to personalize the recommendations for that user.
Providing information that may be used to identify a particular user, such as the user's login name or email address, or a particular client device associated with a user, such the device's MAC (Media Access Control) address or IP (Internet Protocol) address, may raise privacy concerns to some users. Some users may choose to forego the benefit of receiving personalized product recommendations in order to protect their privacy by refusing to provide any information that may be used to identify themselves or their client devices. As a result, such users do not enjoy the full benefits of the services provided by the businesses. At the same time, the businesses may loose profit because some of their customers or users are not aware of certain products the businesses offer that may be of great interest to these customers or users.